1. Field of the Invention
The present invention relates to sound signal enhancement.
2. State of the Art
For the hearing impaired, clearly hearing speech is very difficult for hearing aid wearers, especially in noisy locations. Discrimination of the speech signal is confused because directional cues are not well received or processed by the hearing impaired, and the normal directional cues are poorly preserved by standard hearing aid microphone technologies. For this reason, electronic directionality has been shown to be very beneficial, and directional microphones are becoming common in hearing aids. However, there are limitations to the amount of directionality achievable in microphones alone. Therefore, further benefits are being sought by the use of beamforming techniques, utilizing the multiple microphone signals available for example from a binaural pair of hearing aids.
Beamforming is a method whereby a narrow (or at least narrower) polar directional pattern can be developed by combining multiple signals from spatially separated sensors to create a monaural, or simple, output signal representing the signal from the narrower beam. Another name for this general category of processing is “array processing,” used, for example, in broadside antenna array systems, underwater sonar systems and medical ultrasound imaging systems. Signal processing usually includes the steps of adjusting the phase (or delay) of the individual input signals and then adding (or summing) them together. Sometimes predetermined, fixed amplitude weightings are applied to the individual signals prior to summation, for example to reduce sidelobe amplitudes.
With two sensors, it is possible to create a direction of maximum sensitivity and a null, or direction of minimum sensitivity.
One known beamforming algorithm is described in U.S. Pat. No. 4,956,867, incorporated herein by reference. This algorithm operates to direct a null at the strongest noise source. Since it is assumed that the desired talker signal is from straight ahead, a small region of angles around zero degrees is excluded so that the null is never steered to straight ahead, where it would remove the desired signal. Because the algorithm is adaptive, time is required to find and null out the interfering signal. The algorithm works best when there is a single strong interferer with little reverberation. (Reverberant signals operate to create what appears to be additional interfering signals with many different angles of arrival and times of arrival—i.e., a reverberant signal acts like many simultaneous interferers.) Also, the algorithm works best when an interfering signal is long-lasting—it does not work well for transient interference.
The prior-art beamforming method suffers from serious drawbacks. First, it takes too long to acquire the signal and null it out (adaptation takes too long). Long adaptation time creates a problem with wearer head movements (which change the angle of arrival of the interfering signal) and with transient interfering signals. Second, it does not beneficially reduce the noise in real life situations with numerous interfering signals and/or moderate-to-high reverberation.
A simpler beamforming approach is known from classical beamforming. With only two signals (e.g., in the case of binaural hearing health care, one from the microphone at each ear) classical beamforming simply sums the two signals together. Since it is assumed that the target speech is from straight ahead (i.e., that the hearing aid wearer is looking at the talker), the speech signal in the binaural pair of raw signals is highly correlated, and therefore the sum increases the level of this signal, while the noise sources, assumed to be off-axis, create highly uncorrelated noise signals at each ear. Therefore, there is an enhancement of the desired speech signal over that of the noise signal in the beamformer output. This enhancement is analogous to the increased sensitivity of a broadside array to signals coming from in front as compared to those coming from the side.
This classical beamforming approach still does not optimize the signal-to-noise (voice-to-background) ratio, however, producing only a maximum 3 dB improvement. It is also fixed, and therefore cannot adjust to varying noise conditions.